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U.S. Demand for Chocolate Milk as an Alternative Energy/Sports Drinks
Yang Hu
Texas A&M University
Senarath Dharmasena Texas A&M University
Oral Capps Jr. Texas A&M University,
Ramkumar Janakirarman University of South Carolina,
Selected Paper prepared for presentation at the Southern Agricultural Economics Association (SAEA) Annual Meeting, San Antonio, Texas, February 6-9, 2016.
Copyright 2016 by Yang Hu, Senarath Dharmasena, Oral Capps,Jr, Ramkumar Janakirarman. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.
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ABSTRACT
Consumption of chocolate milk in the United States is growing as an alternative beverage to sports and energy drinks. Recent literature suggests that consumption of chocolate milk vis-à-vis sports and energy drinks is an effective recovery aid after prolonged workouts. In this light, knowledge of price sensitivity, substitutes/complements and demographic profiling with respect to consumption of chocolate milk is important for manufacturers, retailers and advertisers of chocolate milk from a competitive intelligence as well as from a strategic decision-making perspective. Using 62029 household level observations from Nielsen HomeScan Panel for 2011 and Tobit model, factors affecting the demand of chocolate milk was determined. Results show that, chocolate milk is substitute for energy drinks. Factors affecting the probability of purchase of chocolate milk are, price, household size, education status, race, region, the presence of children, gender of household head. The factors affecting the volume of purchase of chocolate milk are price of chocolate milk, household size, education status, race, region, the presence of children, gender of household head. Key Words: Consumer demand, chocolate milk, energy drinks, sports drinks, Nielsen
data, tobit model, censored demand JEL Classification: D11, D12
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INTRODUCTION
Energy drinks market has become a multibillion dollar business in the United States
and they are referred to as an exponentially growing segment in the beverage industry,
second only to bottled water. According to Beverage Marketing Corporation, Energy
drinks consumption advanced by 6.4% in volume from 2013 to 2014, sports drinks
increased 3% in volume, and the sports beverage segment exceeded 1 billion gallons for
the first time in 2011 and topped 1.4 billion gallons in 2014. Sales of energy mixes have
grown by 434% between 2011 and 2013. Sales were ticking up for the energy drink
category according to data from Information Resources Inc. (IRI), Chicago, for the 52
weeks ended Dec. 28, 2014. The $10.5 billion category saw dollar sales increase 4.9%
and units improved 5.4% to 4 billion.
For flavored milk market, according to NPD Group (2010) and Nielsen (2010),
U.S. consumption of chocolate milk is growing and plain chocolate milk servings grew
from 1.2 billion in 2009 to 1.4 billion in 2010. But after the USDA updated school meal
standards, flavored milk was removed from school cafeterias in response to the concerns
about childhood obesity. Because 8% of all fluid milk in the U.S. is consumed in school
and 61.6% of all school milk is chocolate milk, this policy is with the result that milk
consumption plummets by 35%.Furthermore, researchers have found that there’s been a
decade-long decline in the popularity of breakfast cereal market in the United States.
According to 2011-2012 National Center for Health Statistics survey, 15% of U.S. do
not eat breakfast. To a certain extent, skipping breakfast leads to a drop of chocolate
milk consumption because around 20% of milk is used in the cereal. The third
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disadvantage of milk market is a rising competition for chocolate milk with other
beverages, such as protein shakes and soy and almond milks. As a consequence, all these
factors impel chocolate milk industry to look for new target market.
More importantly, many studies have documented over the past couple of years,
showing that chocolate milk was a good substitute for energy or sports drinks. Compared
with energy drinks, researchers find that chocolate milk is better in reducing debilitating
muscle breakdown and increasing endurance. When the runners drank fat free chocolate
milk after a strenuous run, on average, they ran 23% longer and had a 38% increase in
markers of muscle building compared to when they drank a carbohydrate-only sports
beverage with the same amount of calories. Karp (2006) emphasized that chocolate milk
contains high carbohydrate and protein content which are effective for people to recover
from strenuous exercise.
In contrast, one of the most pressing issues of energy drinks is the ingredient
containing many stimulants, such as the caffeine and guarana. In general, energy drinks
encompass sports drinks and nutraceutical drinks but people always use the terms,
energy drinks and sports drinks interchangeably. Excessive consumption of energy
drinks may increase the risk for caffeine overdose and result in greater potential for
acute caffeine toxicity. Initially, the primary consumers of energy drinks were athletes.
However, as the energy drinks market expanded into various niche markets, the majority
of energy drinks are targeted at teenagers and young adults 18 to 34 year old. Kaminer（
2010）said that 30% of youths between ages 12 and 17 regularly consume energy
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drinks. But excessive caffeine is not recommended for people under 18. Although many
brands try to allay consumer’s concerns about caffeine, this fact has triggered increased
negative media coverage and consumers need healthier beverages.
Due to the ingredient advantage of chocolate milk and weakened outlook of milk
market, it is a unique opportunity for chocolate milk processors and retailers to enter the
fastest growing beverage market as recovery drinks. This could provide an additional
occasion for consumers to buy chocolate milk and drive incremental sales. In fact, dairy
industry has been repositioning chocolate milk as a contender in the fast-growing market
for protein bars, shakes and energy beverages. Since 2012, Milk Processor Education
Program (MilkPEP), the group responsible for the “Got Milk?” campaign, has invested
$15 million a year into chocolate-milk campaign to strengthen the role of chocolate milk
as a new-age sports/energy drink. Also, MilkPEP treated their next 20 year campaign as
‘propelling milk back into a position of power’. In 2012, Milkpep launched “My after”
campaign to strengthen the consciousness that consuming low-fat chocolate milk is
better for athletes. Additionally, chocolate milk, like sports or energy drinks, is aligning
with professional athletes and celebrities, incorporating sports games and music to
advertise their products. Recently, NBA stars, professional football players, swimmers
and running groups have been gradually taking chocolate milk as their recovery drink.
Chocolate milk has become the official refuel beverage of many prominent sports
organizations and teams, like IRONMAN® triathlon series, Rock’ n Roll Marathon
series, and Challenged Athletes Foundation.
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While the literature linking chocolate milk benefits with emphasis on the healthy
ingredient and performance edge are abundant, when it comes to the demand analysis for
chocolate milk and sports/ energy drinks, the literature is scarce. Dharmasena and Capps
(2009) used Heckman correction to estimate the demand model for chocolate milk, for
calendar year 2008 in the Nielsen Homescan panels. They found that the own-price
elasticity of demand for chocolate milk was estimated to be -0.04. Factors affecting the
probability of purchase of chocolate milk are price of chocolate milk, household income,
age of household head, education status of household head. Maynard estimated the
flavored milk’s own price elasticity which fell within a range from -1.4 to -1.47 by using
weekly scanner data for the period 1996 through 1998. But they just used this result to
testify whether the price elastic demand for dairy products was increasing. Capps and
Hanselman employed the Barten synthetic demand system to estimate own price, cross-
price, and expenditure elasticities for major energy drink brands by using weekly survey
data from October 2007 to October 2010.
Therefore, a thorough and a complete analysis of demand for chocolate milk and
energy drinks are important due to the lack of demand and price information in the
literature. And the price sensitivity, substitutes or complements and demographic
profiling with respect to consumption of chocolate milk and energy drinks is important
for manufacturers, retailers and advertisers of chocolate milk. In this paper, we will pay
more attention to: (1) determine the factors affecting the purchase of chocolate milk, and
energy or sports drinks (2) estimate the own-price elasticity, cross-rice elasticities and
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income elasticity of chocolate milk and energy/sports drinks (3) once the decision to
purchase chocolate milk is made, to determine the drivers of purchase volume.
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DATA AND METHODOLOGY
The data we used is based on 2011 Nielsen Homescan panel data which provides
detailed beverage-purchase information from 62029 households, including expenditures,
quantities, and socioeconomic demographic characteristic. The table 1 is the summary
statistics for all variables included in the model. We standardized the quantity data as
liquid ounces and the expenditures are express in dollars. Therefore we generated the
price for three beverages in dollars per gallon. Fraction of households did not buy
chocolate milk or energy/sports drinks during the sampling period. In this case, the
amount a household spends on recovery drinks would be zero. If the fraction of the
observations on the dependent variables takes a limit value, the dependent variable is
censored. This kind of consuming behavior leads to corner solutions for some nontrivial
fraction of the population. Application of ordinary least squares to estimate this kind of
regression gives rise to biased estimates even asymptotically (Kennedy 2003). So Tobit
model is indispensable to explicitly model the corner solution dependent variables. Tobit
model is applied to outcome variables that are roughly continuous over positive values
but have a positive probability of equaling zero (Tobin (1958) and Heckman (1979)).
For this data, we do not observe the price of households with zero purchases. Therefore
we used an auxiliary regression to forecast the unavailable price. The price for each
beverage is regressed on household income, household size, and the region. The
parameters estimated from the auxiliary regression are then used to impute prices for the
zero-expenditure observations. It is effective method to address endogenous issue. In
our Tobit model, the independent variables included imputed prices, observed prices,
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household income, presence of children in the household, region, race, employment
status, level of education, gender of household head. Table 2 is the summary statistics
for observed prices and imputed price for each beverage. We found that the observed
prices are consistent with the imputed price. Table 3 is correlation test for three
beverages’ price.
The Tobit model is most easily defined as a latent variable model:
(1) 𝑌𝑖 = 𝑋𝑖β + µi, 𝑋𝑖β + µi > 0 µi~𝑁𝑜𝑟𝑚𝑎𝑙(0,𝜎2)
𝑌𝑖 = 0 𝑋𝑖β + µi ≤ 0
Where i = 1,2,3,……n is the number of observations, 𝑌𝑖 is the censored dependent
variable, 𝑋𝑖 is the vector explanatory variables, β is the vector of unknown parameters to
be estimated. And µi has a normal distribution. For Tobit model, there are two
expectations of dependent variables. If 𝑌𝑖 >0, it is called the “conditional expectation”,
otherwise, called “unconditional expectation”.
(2) Conditional expectation: 𝐸(𝑌|𝑌 > 0,𝑋) = 𝑋β + 𝜎(𝑓(𝑧)𝐹(𝑧))
(3) Unconditional expectation: 𝐸(𝑌|𝑋) = 𝐸(𝑌|𝑌 > 0) ∗ 𝑃(𝑌 > 0|𝑋)
= 𝑋β𝐹(𝑧) + 𝜎(𝑓(𝑧))
Where 𝑍 = 𝑋β/σ,𝜆 = 𝑓(𝑧)𝐹(𝑧) which is called inverse mills ratio, is the ratio between the
standard normal PDF and standard normal CDF. In Tobit model, the coefficients with
each explanatory variable must be transformed into meaningful marginal effects. There
are two types of meaningful marginal effects. The first one is conditional marginal
effects which reflect the marginal effects on consumption that contains the households
actually bought the beverage. The other is unconditional marginal effects for
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consumption of beverage which include all the households whether or not buy the
beverage.
If Xi is a continuous variable, the conditional marginal effect of 𝑋𝑖 on 𝐸(𝑌|𝑌 > 0,𝑋)is
represented by
(4) 𝜕𝐸(𝑌|𝑌 > 0)/𝜕𝑋 = β(1 − 𝑧 𝑓(𝑧)𝐹(𝑧) −
𝑓(𝑧)2
𝐹(𝑧)2)
The unconditional marginal effect of 𝑋𝑖 on 𝐸(𝑌|𝑋) is shown by
(5) 𝜕𝐸(𝑌)/𝜕𝑋 = β𝐹(𝑧)
Therefore,
(6) 𝜕𝐸(𝑌|𝑋)/𝜕𝑋= 𝐹(𝑧) 𝜕𝐸(𝑌|𝑌>0)𝜕𝑋
+ 𝐸(𝑌|𝑌 > 0) 𝜕𝐹(𝑍)𝜕𝑋
So total change in the unconditional expected value of dependent variable 𝑌 is
represented by the sum of (i), the change in the expected value of y being above the limit
weighted by the probability of being above the limit and (ii) the change in the probability
of being above the limit weighted by the expected value of y being above the limit
(McDonald & Moffitt’s (1980)). We tried several functional forms, including linear,
quadratic, and semi-log Tobit model. We found that semi-log model outperformed other
functional forms, except the independent variable-price of chocolate milk used in linear
term for energy drink Tobit model, considering model fit, significance of the variables
(the level of significance used is P-value of 0.05), and Akaike information criterion.
Therefore, we used the semi-log functional form to calculate the conditional and
unconditional marginal effects associated with each explanatory variable and linear
functional form for price of chocolate milk in energy drink demand.
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Conditional marginal effect for semi-log price variable:
(7) 𝜕𝐸(𝑌|𝑌 > 0)/𝜕𝑝 = β/ 𝑝𝑐(1 − 𝑧 𝑓(𝑧)𝐹(𝑧) −
𝑓(𝑧)2
𝐹(𝑧)2)
Unconditional marginal effect for semi-log price variable:
(8) 𝜕𝐸(𝑌)/𝜕𝑝 = β/ 𝑝𝑢𝐹(𝑧)
Where 𝑝𝑐the average price is in the censored sample, 𝑝𝑢 is the average of the
unconditional price.
Therefore
Conditional elasticities:
Own-Price: 𝜀𝑖𝑖𝐶 = β/ 𝑝𝑖𝐶(1 − 𝑧 𝑓(𝑧)𝐹(𝑧) −
𝑓(𝑧)2
𝐹(𝑧)2) 𝑝𝑖𝐶
𝑄𝑖𝐶
Cross-Price: 𝜀𝑖𝑗𝐶 = β/ 𝑝𝑗𝐶(1 − 𝑧 𝑓(𝑧)𝐹(𝑧) −
𝑓(𝑧)2
𝐹(𝑧)2) 𝑝𝑗𝐶
𝑄𝑖𝐶
Income: 𝜀𝐼𝐶 = β/ 𝐼𝑖𝐶(1 − 𝑧 𝑓(𝑧)𝐹(𝑧) −
𝑓(𝑧)2
𝐹(𝑧)2) 𝐼𝑖𝐶
𝑄𝑖𝐶
For the linear price, conditional cross-price elasticity is
𝜀𝑖𝑗𝐶 = β(1− 𝑧 𝑓(𝑧)𝐹(𝑧) −
𝑓(𝑧)2
𝐹(𝑧)2) 𝑝𝑗𝐶
𝑄𝑖𝐶
Unconditional elasticities:
Own-Price: 𝜀𝑖𝑖𝑢 = β/ 𝑝𝑖𝑢𝐹(𝑧) 𝑝𝑖𝑢
𝑄𝑖𝑢
Cross-Price: 𝜀𝑖𝑗𝑢 = β/ 𝑝𝑗𝑢𝐹(𝑧) 𝑝𝑗𝑢
𝑄𝑖𝑢
Income: 𝜀𝐼𝑢 = β/ 𝐼𝑖𝑢𝐹(𝑧) 𝐼𝑖𝑢
𝑄𝑖𝑢
For the linear price, unconditional cross-price elasticity is
𝜀𝑖𝑗𝑢 = βF(𝑧) 𝑝𝑗𝑢
𝑄𝑖𝑢
Where 𝐼𝑐 is conditional mean income and 𝐼𝑢 is unconditional mean income, 𝑄𝑖𝐶 is the
conditional mean of quantity, 𝑄𝑖𝑢 is the unconditional mean of quantity. From equation
6, we could obtain the changes in the probability of being above the limit for
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consumption of each beverage category in response to a change in an explanatory
variable.
(9) 𝜕𝐹(𝑧)/𝜕𝑋 = 1𝐸(𝑌|𝑌>0) ( 𝜕𝐸(𝑌)
𝜕𝑋− 𝐹(𝑧) 𝜕𝐸(𝑌|𝑌>0)
𝜕𝑋)
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EMPIRICAL ESTIMATION
Table 4 is the summary statistics for price, quantity, expenditure and market
penetration for three beverages. From that form, we know that 26.1% households
purchase chocolate milk. Compared with energy drink (7.23% price penetration), 35.7%
household would choose sports drink. Table 5 presents the Tobit regressions results. The
significant economic determinants for chocolate milk are price of chocolate milk, energy
drink, sports drink. The household income did not have a significant effect on the
demand of chocolate milk. In addition, significant demographic independent of demand
of chocolate milk includes household size, education, race, Hispanic origin, region, the
presence of children in a household and gender of the household head.
For energy drink demand, statistically significant determinants are the price of
energy drink, chocolate milk, and sports drink; household size, age, employment status,
education, race, Hispanic origin, region, the presence of children in a household, and
household gender. The household income did not have a significant effect on the
demand of energy drink.
Regarding demand of sports drink, price of chocolate milk, energy drink, and
sports drink significantly affect it. Significant demographic determinants comprise
household size, age, education, race, region, the presence of children in a household,
gender of the household head. Household income is nonsignificant variable for the
demand of sports drink.
Although Tobit model is a regression model, the interpretation of coefficients of
Tobit model is more complicated than OLS regression. Tobit coefficients represent the
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effect of an independent variable on the latent dependent variable of the Tobit model.
Therefore, we transform the coefficients into marginal effects. There are two types of
meaningful marginal effects, conditional marginal effects in equation 4, unconditional
marginal effects in equation5. Conditional marginal effects on the demand of beverage
consider the household who actually bought the beverage into account. On the contrary,
unconditional marginal effects take into account all the households no matter whether
they bought the beverage. The sign of marginal effects are the same as the sign of
coefficients in Tobit model. In order to reduce the influences by outliers and skewed data,
we use the median values to analyze. Table 6 reports the median unconditional marginal
effects. The results of median conditional marginal effects are shown in Table 7. For
brevity, we pay more attention to the results of conditional marginal effects. The
difference between conditional and unconditional marginal effects is unconditional
marginal effects are larger than conditional marginal effects. Table 8 presents the results
of median change in probability of consumption.
For chocolate milk, the average change in probability of consumption for
household size is 0.022, which means if increase one household family number, the
household is 2.2% more likely to consume chocolate milk. A household head had post
college education is less likely to consume chocolate milk compared with the base case
of less than high school education. Compared with white household head, other race is
9.8%~2.2% less likely to consume chocolate milk. Non-Hispanic household head
consume 18.8 more ounces with 2.2% greater probability than Hispanic household head.
For regions, the median changes in probability of consumption when the household head
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located in Middle Atlantic is 0.049. Thus, the household head is 4.9% more likely to
consume chocolate milk than the base case –Pacific. From the table 7, the household
head living in Middle Atlantic consume 42.8 ounces more chocolate milk per year. Other
regions, including East North Central, West north Central, South Atlantic, East south
Central, West south Central, and Mountain, have the same trend like Middle Atlantic.
The presence of children in a household increases the probability of chocolate milk
consumption relative to household without children. Male household heads purchase
about 27 ounces less chocolate milk per year relative to the base case of households
headed by a male and a female.
For energy drink, the household heads with age above 35 years old are
5.6%~15.7% less likely to purchase energy drink. They consume about 83.5~231.6
ounces less energy drinks than the base case of household heads with age less than 25
years. The households who are in full time jobs are 0.8% more likely to buy energy
drink. Education degree significantly affects the consumption of energy drinks. People
with higher education are about 2.1%~6.6% less likely to buy energy drink per year
compared with the base case of households who has less than high school degree.
Household heads who classified as Hispanic are 1.2% more likely to purchase energy
drink. Oriental household heads are 3% less likely to consume energy drink. The regions,
except the mountain part, are all less likely to consume energy drink than the base case
of Pacific part. The presence of children in a household whose age is between 13~17
increases the probability of energy drink. Male household heads are 3.33% more likely
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to purchase energy drink. They purchase 49.3 ounces more energy drink relative to the
base case of households headed by a male and female.
For sports drink, the median changes in probability of consumption when the
household from 55 years to 64 years of age is -0.13. The median changes in probability
of consumption when the household is 64 years or older is -0.18. Thus a household
headed by someone elderly is from13% to 18% less likely to consume sports drink.
From the conditional marginal effect, the household heads who are 55~64 years old
consume 174.8 ounces less sports drinks per year. The household heads that are older
than 64 years consume 249.7 ounces less sports drink compared with the base case of
household heads are less than 25 years old. In addition, the education of household head
significantly affects the demand of sports drink. According to the conditional marginal
effects, households with post -college 51.5 ounces less sports drink per year and they
have a 3.7% lower probability of purchasing sports drink than household with less than a
high school education. Oriental purchase 56.8 ounces less sports drinks per year.
Oriental household heads have a 4.1% less likely to purchase sports drink than the base
case of white household heads. The presence of children in a household whose age is
under 6 years is 4% less likely to purchase sports drink. Regionally, the households
living in South Atlantic are 4.2% more likely to purchase sports drink than the Pacific
part. Female household heads are 5.8% less likely to buy sports drink. The female
household heads purchase 80.9 ounces less sports drinks per year.
Based on the coefficient estimates, we calculated the conditional and
unconditional own-price, cross-price elasticities and income elasticities for all beverages.
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Table 9 represents the mean value of conditional and unconditional elasticities. The
unconditional elasticities estimates are consistently larger than the conditional elasticities.
For chocolate milk, the conditional own-price elasticity is -0.624, which means
that consumers are insensitive to own price changes. The conditional cross-price
elasticities of energy drink and sports drink are -0.091, -0.099, which implies energy
drink and sports drink are complementary beverages for chocolate milk. The conditional
income elasticity of chocolate milk is -0.011 but the income elasticity is not statistically
significant.
For energy drink, the own-price elasticity is -0.599, indicating that energy drink
is less elastic than chocolate milk. The cross-elasticity of chocolate milk and sports drink
are 0.047, -0.072. Therefore, chocolate milk is substitute for energy drink but sports
drink is complementary beverage for energy drink. The income elasticity is 0.004 which
is statistically nonsignificant.
For sports drink, the own-price elasticity is -0.718, indicating that energy drink is
less elastic than energy drink. The cross-elasticity of chocolate milk and sports drink are
-0.038, -0.147. Therefore, chocolate milk and energy drink are complementary for sports
drink. The income elasticity is 0.012 which is not statistically significant.
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CONCLUSIONS
Using household-level purchase data for chocolate milk, energy drink, and sports drink
with related demographic characteristics from the 2011 Nielsen Homescan data, we
estimated three beverage demand models to show that chocolate milk is used as a
substitute for energy drink. In addition, we find that the demographic characteristics of
households have some impact on demand for chocolate milk, energy drink, and sports
drink. The household size, age, education, race, region, the presence of children, gender
of household head are significant determinants of demand for chocolate milk. Energy
drink and sports drink are complementary for chocolate milk. For energy drink demand
model, household size, age, employment status, education, race, region, the presence of
children in a household, gender of household head significantly affect the demand of
energy drink. From estimating the elasticities, we find chocolate milk is a substitute for
energy drink but sports drink is complementary for energy drink. Finally, we estimate
the sports drink demand model. For sports drink, significant demographic variable
includes household size, age, education, race, region, the presence of children, gender of
household head. Chocolate milk and energy drink are complements for sports drink. The
income elasticity of demand demonstrates that energy drink and sports drink are normal
good, however they are not statistically significant.
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REFERENCES
Senarath Dharmasena and Oral Capps, Jr., U.S. Demand for Wellness and Functional Beverages and Implications on Nutritional Intake: An Application of EASI Demand System Capturing Diverse Preference Heterogeniety. 2011-2012 Annual School Channel Survey Godfrey: http://www.foodbev.com/news/vivien-godfrey-and-the-challenges-facing/#.VmH_JLerSM8 Beverage of champions: Chocolate milk gets an Olympic-style makeover, By Jia Lynn Yang January 31, 2014 https://www.washingtonpost.com/business/economy/beverage-of-champions-chocolate-milk-gets-an-olympic-style-makeover/2014/01/31/a13261b6-89c8-11e3-916e-e01534b1e132_story.html http://time.com/3705987/skipping-breakfast-cereal-kellogg/ These 2 Charts Show the Biggest Change in America’s Breakfast. Jack Linshi Lunn WR, Pasiakos SM, Colletto MR, Karfonta KE, Carbone JW, Anderson JM, Rodriguez NR. Chocolate milk & endurance exercise recovery: protein balance, glycogen and performance. Medicine & Science in Sports & Exercise. 2011. [Epub ahead of print] Karp, Jason R.; Johnston, Jeanne D. ; Tecklenburg, Sandra; Mickleborough, Timothy D.; Fly, Alyce D.; Stager, Joel M. Chocolate milk as a post-exercise recovery aid. International Journal of Sport Nutrition & Exercise Metabolism. Feb2006, Vol. 16 Issue 1, p78-91. 14p., Database: Food Science Source Caffeinated energy drinks—A growing problem Chad J. Reissig a, Eric C. Strain a, Roland R. Griffiths a,bEnergy drinks: an assessment of their market size, consumer demographics, ingredient profile, functionality, and regulations in the United States Kaminer Y. Problematic use of energy drinks by adolescents. Child Adolesc Psychiatr Clin N Am. 2010 Jul;19(3):643-50. doi: 10.1016/j.chc.2010.03.015. http://www.marketsandmarkets.com/PressReleases/energy-drinks.asp Jia Lynn Yang, “Beverage of champions: Chocolate milk gets an Olympic-style makeover,” The Washington Post, January, 31, 2014. Thursday, March 27, 2014, Feed Them Milk: A New (and Needed) Approach to Nourishing Americans,http://berryondairy.blogspot.com/2014/03/feed-them-milk-new-and-needed-approach.html (cross promotion method) http://builtwithchocolatemilk.com/
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Is Chocolate Milk the New-Age Energy\Sports Drink in the United States? fragility in dairy product demand analysis Oral Capps, Jraι and Robin D. Hanselmanb, A Pilot Study of the Market for Energy
Drinks
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Table1 Summary Statistics of the Variables in the Model Variable Mean Standard Deviation Price of chocolate milk 0.049 0.024 Price of energy drinks 0.129 0.056 Price of sports drinks 0.052 0.149 Household size 2.36 1.290 Household income 58.32 31.93 Age of household head 25-29 0.018 0.042 Age of household head 30-34 0.038 0.191 Age of household head 35-44 0.147 0.354 Age of household head 45-54 0.276 0.447 Age of household head 55-64 0.297 0.457 Age of household head 65 or older 0.222 0.415 Employment status part-time 0.178 0.383 Employment status full-time 0.390 0.488 Education high school 0.237 0.425 Education undergraduate 0.618 0.485 Education post-college 0.12 0.325 Black 0.094 0.292 Oriental 0.029 0.166 Other 0.040 0.196 Hispanic 0.051 0.220 New England 0.045 0.208 Middle atlantic 0.131 0.337 East north central 0.181 0.385 West north central 0.086 0.281 South atlantic 0.198 0.398
East south central 0.06 0.237 West south central 0.102 0.303 Mountain 0.073 0.260 Children less than 6 hears 0.028 0.164 Children 6-12 years 0.052 0.223 Children 13-17 years 0.067 0.249 Children under 6 and 6-12 years 0.024 0.154 Children under 6 and 13-17 years 0.004 0.064 Children 6-12 and 13-17 years 0.033 0.179 Children under 6, 6-12, and 13-17 0.005 0.070 Female head only 0.250 0.433 Male head only 0.096 0.295
21
Table 3 Correlation Test for Beverage Price Chocolate milk price Energy drinks price Sports drinks price
1.00000
0.13117
<.0001
0.00784
0.0510
0.13117
<.0001
1.00000
-0.00033
0.9347
0.00784
0.0510
-0.00033
0.9347
1.00000
Table 4 summary statistics for price, quantity and market penetration Market
penetration Average
Price Average
conditional quantity(ounce)
Average unconditional
quantity(ounce) Chocolate milk 26.09% 0.049 423 110.38 Energy drink 7.23% 0.13 441.12 31.87 Sports drink 35.78% 0.052 756.55 270.73
Table 2 Summary statistics for observed prices and imputed prices for each beverage
Observed Price (dollars per ounce) Imputed Price (dollars per ounce) Mean Standard Dev Mean Standard Dev
Chocolate milk 0.049 0.024 0.051 0.007 Energy drink 0.129 0.057 0.131 0.007 Sports drink 0.052 0.149 0.110 0.028
22
Table 5 Tobit regression results
Chocolate milk Energy drinks Sports drinks
Variable Estimate P-value Estimate P-value Estimate P-value
Intercept
Price of chocolate milk
Price of energy drink
Price of sports drink
Household size
Household income
Age of household head 25-29
Age of household head 30-34
Age of household head 35-44
Age of household head 45-54
Age of household head 55-64
Age of household head 64 or older
Employment status part-time
Employment status full-time
Education high school
Education undergraduate
Education post-collge
Black
Oriental
Other
Hispanic
New England
Middle Atlantic
East north central
West north central
South Atlantic
East south central
West south central
Mountain
Children less than 6 years
Children 6-12 years
Children 13-17 years
Children under6 and 6-12 years
Children under 6 and 13-17 years
Children 6-12 and 13-17 years
Children under 6, 6-12, 13-17 years
-4813.94
-1008.42
-146.67
-161.25
75.45
-18.49
-37.25
43.27
78.05
100.96
16.20
-187.13
-11.39
-2.43
-27.46
-111.11
-237.18
-336.19
-243.60
-77.73
-74.54
-40.97
169.63
116.35
161.00
63.86
201.58
120.92
-65.70
65.63
155.27
173.56
103.174
150.509
112.65
103.38
<.0001
<.0001
0.0030
<.0001
<.0001
0.0926
0.8037
0.7670
0.5875
0.4818
0.9101
0.1933
0.5185
0.8770
0.5002
0.0056
<.0001
<.0001
<.0001
0.0246
0.0156
0.2667
<.0001
<.0001
<.0001
0.0091
<.0001
<.0001
0.0317
0.1028
<.0001
<.0001
0.0203
0.0893
0.0038
0.2196
-5204.96
2460.87
-1506.67
-179.82
145.63
8.99
-136.99
-205.93
-493.84
-614.89
-962.17
-1369.22
-48.40
73.05
-182.90
-328.261
-576.51
-29.92
-261.00
126.41
105.14
-451.73
-374.22
-401.72
-349.84
-337.75
-298.10
-128.10
-78.48
-198.69
-104.01
265.72
-460.37
-173.20
-152.66
-321.23
<.0001
<.0001
0.0064
<.0001
<.0001
0.6707
0.5370
0.3412
0.0202
0.0037
<.0001
<.0001
0.1739
0.0174
0.0177
<.0001
<.0001
0.4847
0.0004
0.0314
0.0449
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0071
0.1333
0.0069
0.0659
<.0001
<.0001
0.2788
0.0325
0.0349
-6854.30
-83.27
-324.804
-1587.14
166.49
26.43
-81.87
-120.19
-162.82
-239.68
-515.28
-730.09
-14.43
38.59
67.73
-7.43
-151.81
-16.69
-166.33
82.16
22.01
78.22
38.09
7.13
-21.72
170.60
304.09
256.83
123.22
-167.09
100.92
490.59
-319.55
24.31
300.02
-20.57
<.0001
0.0118
<.0001
<.0001
<.0001
0.0619
0.6467
0.4908
0.3429
0.1616
0.0026
<.0001
0.5260
0.0549
0.1931
0.8878
0.0085
0.5448
0.0005
0.0517
0.5553
0.0832
0.2537
0.8273
0.5636
<.0001
<.0001
<.0001
0.0010
0.0010
0.0073
<.0001
<.0001
0.8274
<.0001
0.8469
23
Chocolate milk Energy drinks Sports drinks
Variable Estimate P-value Estimate P-value Estimate P-value
Female head only
Male head only
Sigma
-69.18
-101.56
1141.31
0.0002
<.0001
<.0001
49.42
291.32
1531.57
0.1780
<.0001
<.0001
-236.39
-57.06
1551.58
<.0001
0.0725
<.0001
24
Table 6 median unconditional marginal effects variable Chocolate milk Energy drinks Sports drinks
Household size 14.92 7.71 42.22 Age of household head 25-29 -7.37 -7.25 -20.76 Age of household head 30-34 8.56 -10.9 -30.48 Age of household head 35-44 15.44 -26.14 -41.29 Age of household head 45-54 19.97 -32.55 -60.78 Age of household head 55-64 3.20 -50.94 -130.67
Age of household head 65 &older -37.03 -72.49 -185.14 Employment status part-time -2.25 -2.56 -3.66 Employment status full-time -0.48 3.87 9.79
Education high school -5.43 -9.68 17.68 Education undergraduate -21.98 -17.38 -1.88 Education post-college -46.93 -30.52 -38.50
Black -66.53 -1.58 -4.23 Oriental -48.21 -13.82 -42.18
Other -15.38 6.69 20.93 Hispanic -14.75 5.57 5.58
New England -8.10 -23.91 19.84 Middle Atlantic 33.57 -19.81 9.66
East north central 23.02 -21.27 1.81 West north central 31.86 -18.52 -5.51
South atlantic 12.63 -17.88 43.26 East south central 39.89 -15.78 77.11 West south central 23.93 -6.78 65.13
Mountain -13 -4.15 31.24 Children less than 6 years 12.99 -10.52 -42.37
Children 6-12 years 30.72 -5.51 25.59 Children 13-17 years 34.35 14.07 124.41
Children under6 and 6-12 years 20.41 -24.37 -81.03 Children under 6 and 13-17 hears 29.78 -9.17 6.17
Children 6-12 and 13-17 years 22.29 -8.08 76.08 Children under6,6-12,and 13-17 20.46 -17.01 -5.22
Female head only -13.69 2.61 -59.94 Male head only -20.10 15.422 -14.47
25
Table 7 median conditional marginal effect
variable Chocolate milk Energy drinks Sports drinks Household size 19.04 24.64 56.48
Age of household head 25-29 -9.4 -23.17 -27.78 Age of household head 30-34 10.9 -34.83 -40.77 Age of household head 35-44 19.69 -83.53 -55.24 Age of household head 45-54 25.47 -104.01 -81.31 Age of household head 55-64 4.08 -162.76 -174.81
Age of household head 65 &older -47.22 -231.61 -247.68 Employment status part-time -2.87 -8.19 -4.89 Employment status full-time -0.61 12.36 13.09
Education high school -6.93 -30.94 23.66 Education undergraduate -28.04 -55.53 -2.52 Education post-college -59.85 -97.52 -51.50
Black -84.43 -5.06 -5.67 Oriental -61.47 -44.15 -56.43
Other -19.61 21.38 27.87 Hispanic -18.81 17.78 7.47
New England -10.34 -76.41 26.54 Middle Atlantic 42.81 -63.30 12.92
East north central 29.36 -67.95 2.42 West north central 40.63 -59.18 -7.37
South Atlantic 16.16 -57.13 57.88 East south central 50.87 -50.42 103.16 West south central 0.23 -21.67 87.13
Mountain -16.58 -13.27 41.80 Children less than 6 years 16.56 -33.61 -56.69
Children 6-12 years 39.18 -17.59 34.23 Children 13-17 years 43.79 44.95 166.43
Children under6 and 6-12 years 26.04 -77.88 -108.41 Children under 6 and 13-17 hears 37.98 -29.30 8.25
Children 6-12 and 13-17 years 28.43 -25.82 101.78 Children under6,6-12,and 13-17 26.08 -54.34 -6.98
Female head only -17.46 8.36 -80.19 Male head only -25.63 49.28 -19.36
26
Table 8 median change in probability of consumption variable Chocolate milk Energy drinks Sports drinks
Household size 0.022 0.017 0.041 Age of household head 25-29 -0.01 -0.016 -0.020 Age of household head 30-34 0.013 -0.024 -0.029 Age of household head 35-44 0.023 -0.057 -0.040 Age of household head 45-54 0.029 -0.071 -0.059 Age of household head 55-64 0.004 -0.11 -0.127
Age of household head 65 &older -0.054 -0.157 -0.179 Employment status part-time -0.003 -0.006 -0.004 Employment status full-time -0.001 0.008 0.009
Education high school -0.008 -0.021 0.017 Education undergraduate -0.032 -0.038 -0.002 Education post-college -0.069 -0.066 -0.037
Black -0.098 -0.003 -0.004 Oriental -0.071 -0.030 -0.041
Other -0.023 0.015 0.020 Hispanic -0.022 0.012 0.005
New England -0.012 -0.052 0.019 Middle Atlantic 0.049 -0.043 0.009
East north central 0.034 -0.046 0.002 West north central 0.047 -0.040 -0.005
South atlantic 0.019 -0.039 0.042 East south central 0.059 -0.034 0.075 West south central 0.047 -0.015 0.063
Mountain -0.019 -0.009 0.030 Children less than 6 years 0.019 -0.023 -0.041
Children 6-12 years 0.045 -0.012 0.025 Children 13-17 years 0.050 0.030 0.121
Children under6 and 6-12 years 0.03 -0.053 -0.078 Children under 6 and 13-17 hears 0.044 -0.020 0.006
Children 6-12 and 13-17 years 0.033 -0.017 0.074 Children under6,6-12,and 13-17 0.030 -0.037 -0.005
Female head only -0.020 0.006 -0.058 Male head only -0.029 0.033 -0.014
27
Table 9 Unconditional and Conditional Own-price, Cross-price, and Income Elasticities of Demand for Chocolate milk, Energy drinks, and Sports drinks
Chocolate Milk Energy Drinks Sports Drinks Unconditional Own-Price, Cross-Price, and Income Elasticities
Chocolate Milk -2.049 -0.298 -0.328 Energy Drinks 0.253 -3.079 -0.368
Sports Drinks -0.093 -0.364 -1.778 Income -0.038 0.018 0.030
Conditional Own-Price, Cross-Price, and Income Elasticities
Chocolate Milk -0.624 -0.091 -0.099 Energy Drinks 0.047 -0.599 -0.071 Sports Drink -0.038 -0.147 -0.718 Income -0.011 0.004 0.012
